Text Classification
Transformers
PyTorch
TensorFlow
ONNX
Safetensors
hn
Bengali
Mongolian
xlm-roberta
Text Classification
text-embeddings-inference
Instructions to use seanbenhur/MuLTiGENBiaS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seanbenhur/MuLTiGENBiaS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="seanbenhur/MuLTiGENBiaS")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("seanbenhur/MuLTiGENBiaS") model = AutoModelForSequenceClassification.from_pretrained("seanbenhur/MuLTiGENBiaS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tf_model.h5 from seanbenhur/MuLTiGENBiaS: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/seanbenhur/MuLTiGENBiaS/resolve/main/tf_model.h5
- Command line
-
hf download hf://seanbenhur/MuLTiGENBiaS/tf_model.h5
-
curl -L -o tf_model.h5 https://huggingface.co/seanbenhur/MuLTiGENBiaS/resolve/main/tf_model.h5
1.11 GB
- Xet hash:
- 93c8c1ad9f366f27d593dce75eee0769c7a2412ccf32636c775cc10b8a2e8c54
- Size of remote file:
- 1.11 GB
- SHA256:
- b07feb5a2fff6b0e2fdfc467c6873554f5ab06e31a285b4caa00249b708fe64a
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